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改善监管风险评估中的干预因果预测
1Cox Associates, MoirAI, Entanglement, and University of Colorado, Denver, CO, USA.
Critical reviews in toxicology
|July 25, 2023
概括
美国环保署 (EPA)
科学领域:
- 环境健康科学 环境健康科学
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 美国环境保护局 (EPA) 在2022年发布了一份关于细颗粒物 (PM2.5) 的风险评估.
- 该评估估计了根据修订后的空气质量标准,PM2.5相关的健康风险的潜在减少.
- 这些预测被定义为干预性死亡风险降低的因果预测.
研究的目的:
- 为了评估2022年EPA风险评估中做出的因果预测的有效性.
- 检查两项EPA选择的关键死亡率研究中使用的研究设计和统计方法的适用性.
- 评估在分析的研究中是否符合有效因果推断的条件.
主要方法:
- 审查了EPA用于风险评估的两项长期死亡研究.
- 在这些研究中检查了Cox比例危险 (PH) 模型的应用.
- 评估了有效的干预因果预测所需的四个关键条件:研究设计,因果模型,假设满意度和对非因果因素的调整.
主要成果:
- 使用考克斯PH模型的两项审查的研究没有满足有效因果推断的必要条件.
- 支持干预因果结论的关键要求没有得到满足.
- 这引发了人们对EPA关于PM2.5健康风险的因果预测的解释性和有效性的担忧.
结论:
- 美国环保署选择的PM2.5死亡率研究中使用的研究设计和方法不足以支持干预性因果预测.
- 为了可信的环境健康风险评估,需要在研究设计,因果建模和统计分析方面更加严格.
- 利益相关者应要求提出因果关系主张的风险评估基于适当的干预效应方法.
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